Pandas Profiling Report

Dataset statistics

Number of variables	16
Number of observations	1442
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	170.6 KiB
Average record size in memory	121.2 B

Variable types

DateTime	1
Categorical	4
Numeric	11

Warnings

`username` has constant value "jack"	Constant
`tweet` has a high cardinality: 1438 distinct values	High cardinality
`mentions` is highly correlated with `number of tweets`	High correlation
`video` is highly correlated with `photos`	High correlation
`photos` is highly correlated with `video`	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`number of tweets` is highly correlated with `mentions`	High correlation
`video` is highly correlated with `photos`	High correlation
`photos` is highly correlated with `video`	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`video` is highly correlated with `photos`	High correlation
`photos` is highly correlated with `video`	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`urls` is highly correlated with `mentions` and 1 other fields	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`video` is highly correlated with `photos`	High correlation
`mentions` is highly correlated with `urls` and 2 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`photos` is highly correlated with `video` and 1 other fields	High correlation
`bins` is highly correlated with `percent change`	High correlation
`number of tweets` is highly correlated with `urls` and 2 other fields	High correlation
`hashtags` is highly correlated with `mentions` and 2 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`percent change` is highly correlated with `bins`	High correlation
`cashtags` is highly correlated with `username`	High correlation
`bins` is highly correlated with `username`	High correlation
`username` is highly correlated with `cashtags` and 1 other fields	High correlation
`hashtags` is highly skewed (γ1 = 25.37077893)	Skewed
`tweet` is uniformly distributed	Uniform
`date` has unique values	Unique
`mentions` has 992 (68.8%) zeros	Zeros
`hashtags` has 1168 (81.0%) zeros	Zeros
`video` has 1130 (78.4%) zeros	Zeros
`photos` has 1134 (78.6%) zeros	Zeros
`urls` has 751 (52.1%) zeros	Zeros
`retweets_count` has 42 (2.9%) zeros	Zeros
`percent change` has 27 (1.9%) zeros	Zeros

Reproduction

Analysis started	2021-09-27 19:03:15.735864
Analysis finished	2021-09-27 19:03:40.160606
Duration	24.42 seconds
Software version	pandas-profiling v3.0.0
Download configuration	config.json

date
Date

UNIQUE

Distinct	1442
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	11.4 KiB

Minimum	2016-08-24 16:00:00
Maximum	2021-07-20 09:30:00

Histogram

Histogram with fixed size bins (bins=50)

tweet
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	1438
Distinct (%)	99.7%
Missing	0
Missing (%)	0.0%
Memory size	11.4 KiB

This is great	3
Facts	2
Good morning	2
@dangillmor the process is not as clear as it could be. That's on us. We're taking all the feedback to make it better!	1
https://t.co/wa5c3lv21c	1
Other values (1433)	1433

Length

Max length	11161
Median length	94
Mean length	248.8932039
Min length	1

Characters and Unicode

Total characters	358904
Distinct characters	280
Distinct categories	19 ?
Distinct scripts	5 ?
Distinct blocks	16 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1435 ?
Unique (%)	99.5%

Sample

1st row	@dangillmor the process is not as clear as it could be. That's on us. We're taking all the feedback to make it better!
2nd row	@Stammy @Square @niw @RuaneFootball @martucci @cjburrows @thecleanmachine @goyal_soniya @yuriyr @pon_brandon @LindaxGuo 💯
3rd row	@Jayanta and happy birthday! 🙌🏼🙌🏼🙌🏼 https://t.co/xbd8inh0Ky Power your TouchBistro or Vend point of sale with Square payments and hardware! https://t.co/VYBWJtSyhR
4th row	💬💸 https://t.co/fBhEDusY7a
5th row	"Hey Siri, send John $10" https://t.co/0gbGP0jpzd

Common Values

Value	Count	Frequency (%)
This is great	3	0.2%
Facts	2	0.1%
Good morning	2	0.1%
@dangillmor the process is not as clear as it could be. That's on us. We're taking all the feedback to make it better!	1	0.1%
https://t.co/wa5c3lv21c	1	0.1%
NBA Finals: Raptors and Warriors face off in first Finals game in Canada #NBAFinals https://t.co/Rs8IH3jdbx	1	0.1%
@nedsegal @paraga @boo Smart @paraga @boo I know exactly how he feels.	1	0.1%
Welcome to the flock @dantley! Grateful we get to work w you.	1	0.1%
https://t.co/XDwGVqypgZ Bucks at Raptors #MILvsTOR https://t.co/H41L7NzioH https://t.co/2M8Tc6zc65 @nilsfrahm	1	0.1%
What an amazing group!	1	0.1%
Other values (1428)	1428	99.0%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
to	1683	3.2%
and	1421	2.7%
the	1364	2.6%
a	848	1.6%
we	763	1.5%
of	747	1.4%
you	744	1.4%
for	654	1.3%
this	551	1.1%
is	510	1.0%
Other values (10133)	42752	82.2%

Most occurring characters

Value	Count	Frequency (%)
	52456	14.6%
e	28795	8.0%
t	25478	7.1%
o	21652	6.0%
a	20818	5.8%
n	17490	4.9%
i	17350	4.8%
s	16100	4.5%
r	15898	4.4%
h	11103	3.1%
Other values (270)	131764	36.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	257299	71.7%
Space Separator	52460	14.6%
Uppercase Letter	20706	5.8%
Other Punctuation	20239	5.6%
Decimal Number	4351	1.2%
Final Punctuation	1136	0.3%
Other Symbol	863	0.2%
Connector Punctuation	390	0.1%
Modifier Symbol	223	0.1%
Close Punctuation	220	0.1%
Other values (9)	1017	0.3%

Most frequent character per category

Other Symbol

Value	Count	Frequency (%)
👏	143	16.6%
🙏	99	11.5%
❤	81	9.4%
💯	58	6.7%
⚡	27	3.1%
👋	27	3.1%
👇	27	3.1%
🇬	17	2.0%
👌	16	1.9%
🇳	16	1.9%
Other values (149)	352	40.8%

Lowercase Letter

Value	Count	Frequency (%)
e	28795	11.2%
t	25478	9.9%
o	21652	8.4%
a	20818	8.1%
n	17490	6.8%
i	17350	6.7%
s	16100	6.3%
r	15898	6.2%
h	11103	4.3%
l	10615	4.1%
Other values (19)	72000	28.0%

Uppercase Letter

Value	Count	Frequency (%)
T	2131	10.3%
S	1494	7.2%
I	1400	6.8%
W	1378	6.7%
A	1310	6.3%
C	1065	5.1%
M	928	4.5%
B	847	4.1%
N	825	4.0%
L	766	3.7%
Other values (16)	8562	41.4%

Other Punctuation

Value	Count	Frequency (%)
@	5426	26.8%
/	4770	23.6%
.	4488	22.2%
:	1869	9.2%
,	1306	6.5%
!	979	4.8%
#	431	2.1%
?	344	1.7%
'	333	1.6%
"	68	0.3%
Other values (9)	225	1.1%

Decimal Number

Value	Count	Frequency (%)
1	646	14.8%
0	615	14.1%
2	530	12.2%
4	407	9.4%
3	404	9.3%
5	394	9.1%
7	348	8.0%
9	339	7.8%
6	335	7.7%
8	333	7.7%

Other Letter

Value	Count	Frequency (%)
ツ	6	54.5%
안	1	9.1%
녕	1	9.1%
하	1	9.1%
세	1	9.1%
요	1	9.1%

Math Symbol

Value	Count	Frequency (%)
+	12	48.0%
~	6	24.0%
=	5	20.0%
\|	1	4.0%
↔	1	4.0%

Modifier Symbol

Value	Count	Frequency (%)
🏼	195	87.4%
🏻	13	5.8%
¯	12	5.4%
🏽	3	1.3%

Currency Symbol

Value	Count	Frequency (%)
$	95	97.9%
£	1	1.0%
₿	1	1.0%

Dash Punctuation

Value	Count	Frequency (%)
-	196	93.3%
—	13	6.2%
–	1	0.5%

Format

Value	Count	Frequency (%)
⁩	64	44.8%
⁦	63	44.1%
‍	16	11.2%

Space Separator

Value	Count	Frequency (%)
	52456	> 99.9%
	4	< 0.1%

Initial Punctuation

Value	Count	Frequency (%)
“	156	86.2%
‘	25	13.8%

Final Punctuation

Value	Count	Frequency (%)
’	976	85.9%
”	160	14.1%

Open Punctuation

Value	Count	Frequency (%)
(	206	99.5%
[	1	0.5%

Close Punctuation

Value	Count	Frequency (%)
)	219	99.5%
]	1	0.5%

Connector Punctuation

Value	Count	Frequency (%)
_	390	100.0%

Nonspacing Mark

Value	Count	Frequency (%)
️	136	100.0%

Enclosing Mark

Value	Count	Frequency (%)
⃣	7	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	278005	77.5%
Common	80729	22.5%
Inherited	159	< 0.1%
Katakana	6	< 0.1%
Hangul	5	< 0.1%

Most frequent character per script

Common

Value	Count	Frequency (%)
	52456	65.0%
@	5426	6.7%
/	4770	5.9%
.	4488	5.6%
:	1869	2.3%
,	1306	1.6%
!	979	1.2%
’	976	1.2%
1	646	0.8%
0	615	0.8%
Other values (206)	7198	8.9%

Latin

Value	Count	Frequency (%)
e	28795	10.4%
t	25478	9.2%
o	21652	7.8%
a	20818	7.5%
n	17490	6.3%
i	17350	6.2%
s	16100	5.8%
r	15898	5.7%
h	11103	4.0%
l	10615	3.8%
Other values (45)	92706	33.3%

Hangul

Value	Count	Frequency (%)
안	1	20.0%
녕	1	20.0%
하	1	20.0%
세	1	20.0%
요	1	20.0%

Inherited

Value	Count	Frequency (%)
️	136	85.5%
‍	16	10.1%
⃣	7	4.4%

Katakana

Value	Count	Frequency (%)
ツ	6	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	356124	99.2%
Punctuation	1520	0.4%
None	693	0.2%
Emoticons	137	< 0.1%
VS	136	< 0.1%
Enclosed Alphanum Sup	104	< 0.1%
Dingbats	89	< 0.1%
Misc Symbols	51	< 0.1%
Latin 1 Sup	34	< 0.1%
Katakana	6	< 0.1%
Other values (6)	10	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	52456	14.7%
e	28795	8.1%
t	25478	7.2%
o	21652	6.1%
a	20818	5.8%
n	17490	4.9%
i	17350	4.9%
s	16100	4.5%
r	15898	4.5%
h	11103	3.1%
Other values (79)	128984	36.2%

None

Value	Count	Frequency (%)
🏼	195	28.1%
👏	143	20.6%
💯	58	8.4%
👋	27	3.9%
👇	27	3.9%
👌	16	2.3%
👀	14	2.0%
🏻	13	1.9%
🤔	11	1.6%
💙	10	1.4%
Other values (98)	179	25.8%

Emoticons

Value	Count	Frequency (%)
🙏	99	72.3%
🙌	10	7.3%
😐	5	3.6%
😬	4	2.9%
🙋	4	2.9%
🙄	3	2.2%
😉	3	2.2%
😎	2	1.5%
😮	1	0.7%
😊	1	0.7%
Other values (5)	5	3.6%

Enclosed Alphanum Sup

Value	Count	Frequency (%)
🇬	17	16.3%
🇳	16	15.4%
🆕	13	12.5%
🇯	8	7.7%
🇵	7	6.7%
🇲	6	5.8%
🇺	5	4.8%
🇷	5	4.8%
🇸	4	3.8%
🇧	4	3.8%
Other values (10)	19	18.3%

Punctuation

Value	Count	Frequency (%)
’	976	64.2%
”	160	10.5%
“	156	10.3%
⁩	64	4.2%
⁦	63	4.1%
…	45	3.0%
‘	25	1.6%
‍	16	1.1%
—	13	0.9%
–	1	0.1%

Misc Symbols

Value	Count	Frequency (%)
⚡	27	52.9%
♂	14	27.5%
☺	4	7.8%
♾	2	3.9%
☝	1	2.0%
☕	1	2.0%
☁	1	2.0%
♀	1	2.0%

VS

Value	Count	Frequency (%)
️	136	100.0%

Latin 1 Sup

Value	Count	Frequency (%)
¯	12	35.3%
§	7	20.6%
°	4	11.8%
	4	11.8%
é	3	8.8%
¡	1	2.9%
í	1	2.9%
ã	1	2.9%
£	1	2.9%

Dingbats

Value	Count	Frequency (%)
❤	81	91.0%
✨	2	2.2%
✅	1	1.1%
✊	1	1.1%
✋	1	1.1%
✴	1	1.1%
✖	1	1.1%
✌	1	1.1%

Katakana

Value	Count	Frequency (%)
ツ	6	100.0%

Misc Technical

Value	Count	Frequency (%)
⌚	1	100.0%

Letterlike Symbols

Value	Count	Frequency (%)
™	1	100.0%

Hangul

Value	Count	Frequency (%)
안	1	20.0%
녕	1	20.0%
하	1	20.0%
세	1	20.0%
요	1	20.0%

Arrows

Value	Count	Frequency (%)
↔	1	100.0%

Currency Symbols

Value	Count	Frequency (%)
₿	1	100.0%

Geometric Shapes Ext

Value	Count	Frequency (%)
🟩	1	100.0%

username
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	11.4 KiB

jack	1442

Length

Max length	4
Median length	4
Mean length	4
Min length	4

Characters and Unicode

Total characters	5768
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	jack
2nd row	jack
3rd row	jack
4th row	jack
5th row	jack

Common Values

Value	Count	Frequency (%)
jack	1442	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
jack	1442	100.0%

Most occurring characters

Value	Count	Frequency (%)
j	1442	25.0%
a	1442	25.0%
c	1442	25.0%
k	1442	25.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	5768	100.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
j	1442	25.0%
a	1442	25.0%
c	1442	25.0%
k	1442	25.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	5768	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
j	1442	25.0%
a	1442	25.0%
c	1442	25.0%
k	1442	25.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	5768	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
j	1442	25.0%
a	1442	25.0%
c	1442	25.0%
k	1442	25.0%

mentions
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	17
Distinct (%)	1.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.6463245492

Minimum	0
Maximum	53
Zeros	992
Zeros (%)	68.8%
Negative	0
Negative (%)	0.0%
Memory size	11.4 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	1
95-th percentile	3
Maximum	53
Range	53
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	2.093847025
Coefficient of variation (CV)	3.239621685
Kurtosis	307.465678
Mean	0.6463245492
Median Absolute Deviation (MAD)	0
Skewness	14.33110573
Sum	932
Variance	4.384195364
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=17)

Value	Count	Frequency (%)
0	992	68.8%
1	273	18.9%
2	96	6.7%
3	34	2.4%
4	20	1.4%
5	8	0.6%
8	4	0.3%
7	2	0.1%
11	2	0.1%
6	2	0.1%
Other values (7)	9	0.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	992	68.8%
1	273	18.9%
2	96	6.7%
3	34	2.4%
4	20	1.4%
5	8	0.6%
6	2	0.1%
7	2	0.1%
8	4	0.3%
9	2	0.1%

Value	Count	Frequency (%)
53	1	0.1%
31	1	0.1%
18	1	0.1%
13	1	0.1%
12	1	0.1%
11	2	0.1%
10	2	0.1%
9	2	0.1%
8	4	0.3%
7	2	0.1%

hashtags
Real number (ℝ_≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct	8
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.2871012483

Minimum	0
Maximum	42
Zeros	1168
Zeros (%)	81.0%
Negative	0
Negative (%)	0.0%
Memory size	11.4 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	1
Maximum	42
Range	42
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	1.26368706
Coefficient of variation (CV)	4.40153802
Kurtosis	825.3167325
Mean	0.2871012483
Median Absolute Deviation (MAD)	0
Skewness	25.37077893
Sum	414
Variance	1.596904985
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=8)

Value	Count	Frequency (%)
0	1168	81.0%
1	202	14.0%
2	54	3.7%
3	9	0.6%
4	6	0.4%
5	1	0.1%
42	1	0.1%
6	1	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1168	81.0%
1	202	14.0%
2	54	3.7%
3	9	0.6%
4	6	0.4%
5	1	0.1%
6	1	0.1%
42	1	0.1%

Value	Count	Frequency (%)
42	1	0.1%
6	1	0.1%
5	1	0.1%
4	6	0.4%
3	9	0.6%
2	54	3.7%
1	202	14.0%
0	1168	81.0%

cashtags
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	11.4 KiB

0	1437
1	4
2	1

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	1442
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.1%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	1437	99.7%
1	4	0.3%
2	1	0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	1437	99.7%
1	4	0.3%
2	1	0.1%

Most occurring characters

Value	Count	Frequency (%)
0	1437	99.7%
1	4	0.3%
2	1	0.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	1442	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	1437	99.7%
1	4	0.3%
2	1	0.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	1442	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	1437	99.7%
1	4	0.3%
2	1	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1442	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	1437	99.7%
1	4	0.3%
2	1	0.1%

video
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	8
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.3210818308

Minimum	0
Maximum	7
Zeros	1130
Zeros (%)	78.4%
Negative	0
Negative (%)	0.0%
Memory size	11.4 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	2
Maximum	7
Range	7
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.7681819305
Coefficient of variation (CV)	2.392480224
Kurtosis	18.77912957
Mean	0.3210818308
Median Absolute Deviation (MAD)	0
Skewness	3.694846762
Sum	463
Variance	0.5901034784
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=8)

Value	Count	Frequency (%)
0	1130	78.4%
1	226	15.7%
2	51	3.5%
3	19	1.3%
4	8	0.6%
5	4	0.3%
6	2	0.1%
7	2	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1130	78.4%
1	226	15.7%
2	51	3.5%
3	19	1.3%
4	8	0.6%
5	4	0.3%
6	2	0.1%
7	2	0.1%

Value	Count	Frequency (%)
7	2	0.1%
6	2	0.1%
5	4	0.3%
4	8	0.6%
3	19	1.3%
2	51	3.5%
1	226	15.7%
0	1130	78.4%

photos
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	12
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.3765603329

Minimum	0
Maximum	13
Zeros	1134
Zeros (%)	78.6%
Negative	0
Negative (%)	0.0%
Memory size	11.4 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	2
Maximum	13
Range	13
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	1.057187731
Coefficient of variation (CV)	2.80748565
Kurtosis	47.09885571
Mean	0.3765603329
Median Absolute Deviation (MAD)	0
Skewness	5.693583242
Sum	543
Variance	1.117645898
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=12)

Value	Count	Frequency (%)
0	1134	78.6%
1	208	14.4%
2	48	3.3%
3	24	1.7%
4	14	1.0%
7	4	0.3%
5	3	0.2%
8	2	0.1%
13	2	0.1%
6	1	0.1%
Other values (2)	2	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1134	78.6%
1	208	14.4%
2	48	3.3%
3	24	1.7%
4	14	1.0%
5	3	0.2%
6	1	0.1%
7	4	0.3%
8	2	0.1%
9	1	0.1%

Value	Count	Frequency (%)
13	2	0.1%
11	1	0.1%
9	1	0.1%
8	2	0.1%
7	4	0.3%
6	1	0.1%
5	3	0.2%
4	14	1.0%
3	24	1.7%
2	48	3.3%

urls
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	11
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.7593619972

Minimum	0
Maximum	16
Zeros	751
Zeros (%)	52.1%
Negative	0
Negative (%)	0.0%
Memory size	11.4 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	1
95-th percentile	3
Maximum	16
Range	16
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.152343842
Coefficient of variation (CV)	1.517515818
Kurtosis	28.86571183
Mean	0.7593619972
Median Absolute Deviation (MAD)	0
Skewness	3.68603954
Sum	1095
Variance	1.327896331
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=11)

Value	Count	Frequency (%)
0	751	52.1%
1	463	32.1%
2	142	9.8%
3	48	3.3%
4	15	1.0%
5	14	1.0%
6	3	0.2%
7	3	0.2%
16	1	0.1%
10	1	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	751	52.1%
1	463	32.1%
2	142	9.8%
3	48	3.3%
4	15	1.0%
5	14	1.0%
6	3	0.2%
7	3	0.2%
9	1	0.1%
10	1	0.1%

Value	Count	Frequency (%)
16	1	0.1%
10	1	0.1%
9	1	0.1%
7	3	0.2%
6	3	0.2%
5	14	1.0%
4	15	1.0%
3	48	3.3%
2	142	9.8%
1	463	32.1%

replies_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	533
Distinct (%)	37.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	549.4036061

Minimum	0
Maximum	68375
Zeros	4
Zeros (%)	0.3%
Negative	0
Negative (%)	0.0%
Memory size	11.4 KiB

Quantile statistics

Minimum	0
5-th percentile	5
Q1	27
median	81
Q3	243.75
95-th percentile	1562.5
Maximum	68375
Range	68375
Interquartile range (IQR)	216.75

Descriptive statistics

Standard deviation	3033.749321
Coefficient of variation (CV)	5.521895538
Kurtosis	244.7361389
Mean	549.4036061
Median Absolute Deviation (MAD)	68
Skewness	14.16221557
Sum	792240
Variance	9203634.942
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
3	24	1.7%
8	23	1.6%
6	22	1.5%
9	21	1.5%
11	20	1.4%
4	20	1.4%
5	18	1.2%
12	16	1.1%
19	14	1.0%
10	14	1.0%
Other values (523)	1250	86.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	4	0.3%
1	8	0.6%
2	14	1.0%
3	24	1.7%
4	20	1.4%
5	18	1.2%
6	22	1.5%
7	12	0.8%
8	23	1.6%
9	21	1.5%

Value	Count	Frequency (%)
68375	1	0.1%
43568	1	0.1%
38045	1	0.1%
35556	1	0.1%
31458	1	0.1%
27566	1	0.1%
22342	1	0.1%
21813	1	0.1%
19264	1	0.1%
12851	1	0.1%

retweets_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	609
Distinct (%)	42.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1173.194175

Minimum	0
Maximum	151587
Zeros	42
Zeros (%)	2.9%
Negative	0
Negative (%)	0.0%
Memory size	11.4 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	27
median	113
Q3	366.75
95-th percentile	3147.85
Maximum	151587
Range	151587
Interquartile range (IQR)	339.75

Descriptive statistics

Standard deviation	8060.152803
Coefficient of variation (CV)	6.870263233
Kurtosis	231.2194763
Mean	1173.194175
Median Absolute Deviation (MAD)	107
Skewness	14.34012873
Sum	1691746
Variance	64966063.22
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2	47	3.3%
1	45	3.1%
0	42	2.9%
3	34	2.4%
4	28	1.9%
5	20	1.4%
6	14	1.0%
8	13	0.9%
27	10	0.7%
22	10	0.7%
Other values (599)	1179	81.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	42	2.9%
1	45	3.1%
2	47	3.3%
3	34	2.4%
4	28	1.9%
5	20	1.4%
6	14	1.0%
7	8	0.6%
8	13	0.9%
9	8	0.6%

Value	Count	Frequency (%)
151587	1	0.1%
143448	1	0.1%
137959	1	0.1%
90853	1	0.1%
82384	1	0.1%
71494	1	0.1%
53941	1	0.1%
40647	1	0.1%
36622	1	0.1%
32642	1	0.1%

likes_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	1095
Distinct (%)	75.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5022.89251

Minimum	1
Maximum	778372
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	11.4 KiB

Quantile statistics

Minimum	1
5-th percentile	20
Q1	217
median	661
Q3	2069.5
95-th percentile	18770.2
Maximum	778372
Range	778371
Interquartile range (IQR)	1852.5

Descriptive statistics

Standard deviation	28776.07576
Coefficient of variation (CV)	5.728984982
Kurtosis	419.6634652
Mean	5022.89251
Median Absolute Deviation (MAD)	580.5
Skewness	18.2259457
Sum	7243011
Variance	828062536
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
17	7	0.5%
14	7	0.5%
11	7	0.5%
16	6	0.4%
19	5	0.3%
6	5	0.3%
59	5	0.3%
35	5	0.3%
9	5	0.3%
20	5	0.3%
Other values (1085)	1385	96.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	2	0.1%
3	2	0.1%
4	3	0.2%
5	2	0.1%
6	5	0.3%
7	4	0.3%
8	3	0.2%
9	5	0.3%
10	3	0.2%
11	7	0.5%

Value	Count	Frequency (%)
778372	1	0.1%
451099	1	0.1%
311529	1	0.1%
293496	1	0.1%
193073	1	0.1%
161974	1	0.1%
141080	1	0.1%
115108	1	0.1%
111972	1	0.1%
104797	1	0.1%

number of tweets
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	36
Distinct (%)	2.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.441054092

Minimum	1
Maximum	162
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	11.4 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1
median	2
Q3	3
95-th percentile	11
Maximum	162
Range	161
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	7.382552043
Coefficient of variation (CV)	2.145433302
Kurtosis	202.5780047
Mean	3.441054092
Median Absolute Deviation (MAD)	1
Skewness	11.92597833
Sum	4962
Variance	54.50207467
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=36)

Value	Count	Frequency (%)
1	646	44.8%
2	306	21.2%
3	151	10.5%
4	97	6.7%
5	54	3.7%
6	37	2.6%
7	31	2.1%
8	19	1.3%
10	15	1.0%
11	13	0.9%
Other values (26)	73	5.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	646	44.8%
2	306	21.2%
3	151	10.5%
4	97	6.7%
5	54	3.7%
6	37	2.6%
7	31	2.1%
8	19	1.3%
9	10	0.7%
10	15	1.0%

Value	Count	Frequency (%)
162	1	0.1%
111	1	0.1%
97	1	0.1%
57	1	0.1%
56	1	0.1%
46	1	0.1%
43	2	0.1%
40	2	0.1%
38	1	0.1%
37	1	0.1%

price
Real number (ℝ_≥0)

Distinct	1211
Distinct (%)	84.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	31.50781671

Minimum	14.30000019
Maximum	76.87000275
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	11.4 KiB

Quantile statistics

Minimum	14.30000019
5-th percentile	16.00150003
Q1	19.83999968
median	31.23666668
Q3	36.25749874
95-th percentile	57.18533293
Maximum	76.87000275
Range	62.57000256
Interquartile range (IQR)	16.41749907

Descriptive statistics

Standard deviation	12.4813959
Coefficient of variation (CV)	0.3961364895
Kurtosis	1.34750385
Mean	31.50781671
Median Absolute Deviation (MAD)	7.241666794
Skewness	1.042551359
Sum	45434.2717
Variance	155.7852437
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
18.14999962	5	0.3%
18.45000076	5	0.3%
34.75	5	0.3%
31.90999985	4	0.3%
30.5	4	0.3%
18	4	0.3%
32.86999893	4	0.3%
29.85000038	4	0.3%
16.95000076	3	0.2%
32.84999847	3	0.2%
Other values (1201)	1401	97.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
14.30000019	2	0.1%
14.31000042	1	0.1%
14.31333319	1	0.1%
14.32000001	1	0.1%
14.33999983	1	0.1%
14.34000015	1	0.1%
14.35999966	2	0.1%
14.39000034	1	0.1%
14.42000008	1	0.1%
14.43999958	1	0.1%

Value	Count	Frequency (%)
76.87000275	1	0.1%
76.61000061	1	0.1%
73.16999817	1	0.1%
73.09999847	1	0.1%
73.05000305	1	0.1%
72.5566686	1	0.1%
72.51000214	1	0.1%
72.27999878	1	0.1%
71.06999969	1	0.1%
70.86000061	1	0.1%

percent change
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct	1413
Distinct (%)	98.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.001164469108

Minimum	-0.1795004093
Maximum	0.2690450286
Zeros	27
Zeros (%)	1.9%
Negative	650
Negative (%)	45.1%
Memory size	11.4 KiB

Quantile statistics

Minimum	-0.1795004093
5-th percentile	-0.02633335287
Q1	-0.007303437164
median	0.000839321342
Q3	0.009306136269
95-th percentile	0.03010720506
Maximum	0.2690450286
Range	0.4485454379
Interquartile range (IQR)	0.01660957343

Descriptive statistics

Standard deviation	0.0224419658
Coefficient of variation (CV)	19.27227235
Kurtosis	23.71171879
Mean	0.001164469108
Median Absolute Deviation (MAD)	0.008339942576
Skewness	0.7783398446
Sum	1.679164454
Variance	0.0005036418288
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	27	1.9%
0.002958647665	2	0.1%
-0.001601317623	2	0.1%
0.01182736634	2	0.1%
-0.02925527975	1	0.1%
-0.01400108956	1	0.1%
0.02874969905	1	0.1%
0.00765233064	1	0.1%
-0.001926878778	1	0.1%
0.01108037515	1	0.1%
Other values (1403)	1403	97.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-0.1795004093	1	0.1%
-0.132510452	1	0.1%
-0.1233974119	1	0.1%
-0.1186813388	1	0.1%
-0.1162518607	1	0.1%
-0.1147373753	1	0.1%
-0.1012580307	1	0.1%
-0.0875292677	1	0.1%
-0.08402687431	1	0.1%
-0.08248886624	1	0.1%

Value	Count	Frequency (%)
0.2690450286	1	0.1%
0.1540526553	1	0.1%
0.1372548531	1	0.1%
0.1213535588	1	0.1%
0.09404011522	1	0.1%
0.09031622248	1	0.1%
0.08396610627	1	0.1%
0.07433101969	1	0.1%
0.07251031929	1	0.1%
0.07047191572	1	0.1%

bins
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	1.7 KiB

no change	1208
rise	129
drop	105

Length

Max length	9
Median length	9
Mean length	8.188626907
Min length	4

Characters and Unicode

Total characters	11808
Distinct characters	13
Distinct categories	2 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	drop
2nd row	no change
3rd row	no change
4th row	no change
5th row	no change

Common Values

Value	Count	Frequency (%)
no change	1208	83.8%
rise	129	8.9%
drop	105	7.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
no	1208	45.6%
change	1208	45.6%
rise	129	4.9%
drop	105	4.0%

Most occurring characters

Value	Count	Frequency (%)
n	2416	20.5%
e	1337	11.3%
o	1313	11.1%
	1208	10.2%
c	1208	10.2%
h	1208	10.2%
a	1208	10.2%
g	1208	10.2%
r	234	2.0%
i	129	1.1%
Other values (3)	339	2.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	10600	89.8%
Space Separator	1208	10.2%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
n	2416	22.8%
e	1337	12.6%
o	1313	12.4%
c	1208	11.4%
h	1208	11.4%
a	1208	11.4%
g	1208	11.4%
r	234	2.2%
i	129	1.2%
s	129	1.2%
Other values (2)	210	2.0%

Space Separator

Value	Count	Frequency (%)
	1208	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	10600	89.8%
Common	1208	10.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
n	2416	22.8%
e	1337	12.6%
o	1313	12.4%
c	1208	11.4%
h	1208	11.4%
a	1208	11.4%
g	1208	11.4%
r	234	2.2%
i	129	1.2%
s	129	1.2%
Other values (2)	210	2.0%

Common

Value	Count	Frequency (%)
	1208	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	11808	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
n	2416	20.5%
e	1337	11.3%
o	1313	11.1%
	1208	10.2%
c	1208	10.2%
h	1208	10.2%
a	1208	10.2%
g	1208	10.2%
r	234	2.0%
i	129	1.1%
Other values (3)	339	2.9%

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	date	tweet	username	mentions	hashtags	video	photos	urls	replies_count	retweets_count	likes_count	number of tweets	price	percent change	bins
0	2016-08-24 16:00:00	@dangillmor the process is not as clear as it could be. That's on us. We're taking all the feedback to make it better!	jack	0	0	0	0	0	14	3	17	1	18.250000	-0.029255	drop
1	2016-08-25 09:30:00	@Stammy @Square @niw @RuaneFootball @martucci @cjburrows @thecleanmachine @goyal_soniya @yuriyr @pon_brandon @LindaxGuo 💯	jack	9	0	0	0	0	16	1	26	1	18.330000	0.004384	no change
2	2016-08-29 16:00:00	@Jayanta and happy birthday! 🙌🏼🙌🏼🙌🏼 https://t.co/xbd8inh0Ky Power your TouchBistro or Vend point of sale with Square payments and hardware! https://t.co/VYBWJtSyhR	jack	1	0	0	0	2	37	83	270	3	18.469999	0.004350	no change
3	2016-08-31 09:30:00	💬💸 https://t.co/fBhEDusY7a	jack	0	0	0	0	1	24	61	168	1	18.389999	0.000544	no change
4	2016-09-01 16:00:00	"Hey Siri, send John $10" https://t.co/0gbGP0jpzd	jack	0	0	0	0	1	24	115	250	1	19.500000	0.006711	no change
5	2016-09-02 09:30:00	YES 🙏🏼🙏🏼🙏🏼 https://t.co/N3qwCQXbIx https://t.co/53EONK167q	jack	0	0	1	1	1	20	24	118	1	19.620001	0.006154	no change
6	2016-09-07 09:30:00	@IStandWithAhmed @JesseDorogusker great meeting you Ahmed!	jack	0	0	0	0	0	9	11	55	1	20.049999	0.006021	no change
7	2016-09-08 09:30:00	🆕 from @120Sports! https://t.co/cdLhHr14uK	jack	1	0	0	0	1	17	46	85	1	18.809999	-0.053347	drop
8	2016-09-10 16:00:00	Watch 🏈 LIVE on Twitter #AFvsGSU https://t.co/Ee7xEKAotX “Deskbound” by Kelly Starrett https://t.co/CyhN1tfme2	jack	0	1	0	0	2	51	103	236	2	18.123334	-0.007122	no change
9	2016-09-12 16:00:00	Happy Birthday @adambain! 🎂👟🏀	jack	1	0	0	0	0	30	30	301	1	18.150000	0.010579	no change

Last rows

	date	tweet	username	mentions	hashtags	cashtags	video	photos	urls	replies_count	retweets_count	likes_count	number of tweets	price	percent change	bins
1432	2021-07-09 09:30:00	@JonLetchford Looking at this @papiflorida No @0xAfterburner That’s the plan @BrokenUniscorn That’s the plan @deyonte_btc That’s the plan We’re doing it #Bitcoin Mike was an incredible light for everyone around him. Grateful for the time we had. And a reminder to express it more while we’re together. 💔	jack	0	1	0	0	0	0	661	942	6596	7	67.070000	0.003591	no change
1433	2021-07-09 16:00:00	@elirousso @CashApp Thank you!	jack	0	0	0	0	0	0	34	12	306	1	68.970001	0.028329	rise
1434	2021-07-14 16:00:00	@nic__carter Nah 👋🏼	jack	0	0	0	0	0	0	1613	2029	32017	2	70.269997	-0.002130	no change
1435	2021-07-16 09:30:00	Ok we made a twitter: @tbd54566975 @bits_infinity @b_buzzkill Was hoping you’d know @nahaarisnotrust $SQ @donovan_hat 🤔 @abbasi_z Too Bad Dude @themstems To Be Dynamic @Gardner ¯\_(ツ)_/¯ This @b_buzzkill Why wouldn’t you @Jac0van It is TBD @stephanlivera This is the (only) way @wongmjane Much better We’ll set up Twitter and github accounts soon and update this thread on where to find them. How is this different from @SqCrypto? Square doesn’t give direction to @SqCrypto, only funding. They chose to work on LDK, and are doing an incredible job! TBD will be focused on creating a platform business, and will open source our work along the way. Like our new #Bitcoin hardware wallet, we’re going to do this completely in the open. Open roadmap, open development, and open source. @brockm is leading and building this team, and we have some ideas around the initial platform primitives we want to build. Square is creating a new business (joining Seller, Cash App, & Tidal) focused on building an open developer platform with the sole goal of making it easy to create non-custodial, permissionless, and decentralized financial services. Our primary focus is #Bitcoin. Its name is TBD.	jack	4	2	1	0	0	0	3451	8224	59187	16	68.559998	0.007198	no change
1436	2021-07-16 16:00:00	@BustaRhymes https://t.co/FjiaS84QH2 @m_tmkns 🙏🏼	jack	0	0	0	0	0	1	97	274	2301	2	66.410004	-0.031359	drop
1437	2021-07-17 09:30:00	https://t.co/L49eiWThl9	jack	0	0	0	0	0	1	282	312	1732	1	67.453331	0.015710	no change
1438	2021-07-17 16:00:00	❤️ https://t.co/c4yEfiys5v	jack	0	0	0	0	0	1	294	177	1600	1	66.280001	-0.017395	no change
1439	2021-07-18 16:00:00	@MikeTyson https://t.co/0j3aZAXcwp	jack	0	0	0	0	0	1	27	76	643	1	66.149999	-0.002964	no change
1440	2021-07-19 16:00:00	@elonmusk @BitcoinMagazine @CathieDWood Can I borrow a wig?	jack	0	0	0	0	0	0	399	378	9588	1	66.019997	0.011956	no change
1441	2021-07-20 09:30:00	Square Banking is live! Checking, savings, debit card for small businesses: https://t.co/FacoHOogPd https://t.co/Sip86oI6fU @elonmusk @BitcoinMagazine @CathieDWood Or we go with this one instead: https://t.co/c6yN8q09hA	jack	0	0	0	1	1	2	665	847	7807	3	66.250000	0.003484	no change

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